Tubular structure segmentation

ABSTRACT

Described herein are systems, methods, and instrumentalities associated with image segmentation such as tubular structure segmentation. An artificial neural network is trained to segment tubular structures of interest in a medical scan image based on annotated images of a different type of tubular structures that may have a different contrast and/or appearance from the tubular structures of interest. The training may be conducted in multiple stages during which a segmentation model learned from the annotated images during a first stage may be modified to fit the tubular structures of interest in a second stage. In examples, the tubular structures of interest may include coronary arteries, catheters, guide wires, etc., and the annotated images used for training the artificial neural network may include blood vessels such as retina blood vessels.

BACKGROUND

Segmenting tubular structures such as arteries, veins, catheters, guidewires, etc. in a medical scan image (e.g., an X-ray fluoroscopic image)may be essential for many downstream image processing tasks including,for example, visibility enhancement, multi-modal image registration,road-mapping, etc. In recent years, deep learning based segmentationtechniques have been increasingly adopted in the medical imaging fieldand have shown superior performance than conventional image segmentationtechniques. Due to the scarcity of labeled training data for tubularstructures, however, these deep learning based technique have not beenapplied in segmentation tasks involving commonly seen tubular structuressuch as those described above. Accordingly, it is highly desirable todevelop deep learning based systems and instrumentalities based onpresently available tubular training data that may be limited to aspecific type of tubular structures and transfer or adapt the knowledgelearned from these training data to other types of tubular structures.

SUMMARY

Described herein are systems, methods, and instrumentalities associatedwith the segmentation of tubular structures. An apparatus configured toperform the segmentation task may include one or more processors thatmay be configured to receive a medical image (e.g., an X-rayfluoroscopic image) depicting a first type of tubular structures (e.g.,catheters, guide wires, etc.), and segment the first type of tubularstructures from the medical image using an artificial neural network(ANN). The ANN may be trained to segment the first type of tubularstructures through a process that may comprise training the ANN during afirst stage of the process to segment a second type of tubularstructures (e.g., retina blood vessels) based on annotated medicalimages of the second type of tubular structures, and further trainingthe ANN during a second stage of the process to segment the first typeof tubular structures based on a segmentation model learned from thefirst stage of the training process. The second stage of the trainingprocess may comprise providing a first training image comprising thefirst type of tubular structures to the ANN, causing the ANN to generatea first segmentation of the first type of tubular structures based onthe segmentation model learned from the first stage of the trainingprocess, correcting the first segmentation generated by the ANN based onone or more characteristics of the first type of tubular structures toderive a corrected segmentation, and causing the ANN to adjust thesegmentation model based on a difference between the first segmentationgenerated by the ANN and the corrected segmentation.

In examples, correcting the first segmentation based on the one or morecharacteristics of the first type of tubular structures may compriseidentifying one or more connected regions that correspond to the firsttype of tubular structures in the first segmentation, determining arespective size of each of the one or more connected regions, andindicating, in the corrected segmentation, whether each of the one ormore connected regions includes the first type of tubular structuresbased on the size of the connected region. For example, a connectedregion may be indicated as including the first type of tubularstructures if the size of the connected region is above a threshold andthe connected region may be indicated as not including the first type oftubular structures if the size of the connected region is below thethreshold. In examples, the size of a connected region may be determinedbased on the number of pixels comprised in the connected region, andidentifying a connected region that may correspond to the first type oftubular structures in the first segmentation may comprise identifying aconsecutive number of pixels in the first segmentation that areindicated as belonging to the first type of tubular structures andtreating the area occupied by the consecutive number of pixels as theconnected region.

In examples, correcting the first segmentation based on the one or morecharacteristics of the first type of tubular structures may comprisedetermining a motion of an area of the first segmentation that may beindicated as including the first type of tubular structures, andindicating, in the corrected segmentation, whether the area includes thefirst type of tubular structures based on the motion of the area. Inexamples, the motion of an area may be determined by calculating achange (e.g., a signal change, a pixel-wise displacement, etc.) betweena first training image comprising the first type of tubular structuresand a second training image comprising the first type of tubularstructures. The motion of the area may be determined by firstregistering the first training image with the second training image.Using these techniques, the area may continue to be labeled as includingthe first type of tubular structures if the area has a larger motionthan a background area of the first segmentation. Conversely, the areamay be re-labeled as not including the first type of tubular structuresif the area has a substantially similar motion as a background area ofthe first segmentation.

BRIEF DESCRIPTION OF THE DRAWINGS

A more detailed understanding of the examples disclosed herein may behad from the following description, given by way of example inconjunction with the accompanying drawing.

FIG. 1 is a diagram illustrating an example of a tubular structuresegmentation (TSS) system or apparatus in accordance with one or moreembodiments describe herein.

FIG. 2A is a diagram illustrating the training of an artificial neuralnetwork using annotated medical images of a certain type of tubularstructures, and FIG. 2B is a simplified diagram illustrating furthertraining the artificial neural network to process a different type oftubular structures without annotated medical images of the differenttype of tubular structures.

FIG. 3A is a diagram illustrating an example technique for correcting asegmentation generated by an artificial neural network based on therespective sizes of one or more connected regions of the segmentation.

FIG. 3B is a diagram illustrating an example technique for correcting asegmentation generated by an artificial neural network based on motioninformation associated with identified tubular structures in thesegmentation.

FIG. 4 is a flow diagram illustrating example operations that may beperformed for training a neural network in accordance with one or moreembodiments described herein.

FIG. 5 is a block diagram illustrating example components of anapparatus that may be configured to perform the tubular structuresegmentation tasks described herein.

DETAILED DESCRIPTION

The present disclosure is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 illustrate an example of a tubular structure segmentation (TSS)system or apparatus (e.g., TSS 102) that may be configured to receivemedical image 104 comprising a plurality of tubular structures andobtain segmentation 106 of the tubular structures using deep learningbased techniques. Medical image 104 may include a medical scan imagesuch as an X-ray fluoroscopic image of a human body and the tubularstructures depicted in image 104 may include anatomical tubularstructures of the human body (e.g., blood vessels) and/or artificiallytubular structures placed into the human body (e.g., catheters, guidewires, etc.). TSS 102 may include an artificial neural network (ANN)pre-trained to segment the tubular structures from medical image 104. Inexamples, the ANN may include a convolutional neural network (CNN)(e.g., with a U-Net structure) having multiple convolutional layers, oneor more pooling layers, and/or one or more fully-connected layers. Theconvolutional layers may be followed by batch normalization layersand/or linear or non-linear activation functions (e.g., such asrectified linear unit or ReLU activation functions). Each of theconvolutional layers may include a plurality of convolution kernels orfilters with respective weights, the values of which may be learnedthrough a training process so as to extract features from medical image104. The features extracted by the convolutional layers may bedown-sampled through one or more pooling layers to obtain arepresentation of the features, for example, in the form of a featuremap or a feature vector. The CNN may further include one or moreun-pooling layers and one or more transposed convolutional layers.Through the un-pooling layers, the CNN may up-sample the featuresextracted from medical image 104 and process the up-sampled featuresthrough the one or more transposed convolutional layers (e.g., via aplurality of deconvolution operations) to derive an up-scaled or densefeature map or feature vector. The dense feature map or vector may thenbe used to predict areas (e.g., pixels) of medical image 104 that belongto the tubular structures.

As will be described in greater detail below, the operating parametersof the ANN (e.g., weights of the various filters or kernels of the ANN)associated with segmenting the tubular structures from medical image 104may be learned through a training process (e.g., an offline trainingprocess) that may be conducted using labeled training images of adifferent type of tubular structures. Further, even though only oneinput medical image is shown in FIG. 1 , those skilled in the art willappreciate that TSS may be configured to process multiple medicalimages, for example, either sequentially or in parallel.

Segmentation 106 may be obtained (e.g., generated) in different formatsincluding, for example, in the form of one or more segmentation masks orone or more binary images. For example, a binary image generated by TSS102 may include pixels that correspond to the pixels of medical image104, and the pixels in the binary image that belong to the identifiedtubular structures may be assigned a first value (e.g., 1) while thosebelonging to non-tubular areas (e.g., background pixels) may be assigneda second value (e.g., 0).

FIG. 2A and FIG. 2B illustrate example techniques that may be used totrain an artificial neural network (e.g., the ANN described with respectto TSS 102 of FIG. 1 ) to segment tubular structures from a medicalimage. FIG. 2A illustrates that the artificial neural network may betrained first using a first training dataset comprising labeled medicalimages of a certain type (e.g., a second type) of tubular structures andFIG. 2B illustrates that the artificial neural network trained using thefirst training dataset may be further trained to process (e.g., segment)other types (e.g., a first type) of tubular structures without havinglabeled training images of the other types of tubular structures. Aswill be described in greater detail below, these example trainingtechniques may allow the artificial neural network to acquire (e.g.,learn) the ability to segment multiple types of tubular structuresdespite only having labeled training data for a specific type of tubularstructures.

Referring to FIG. 2A, ANN 202 may be trained initially (e.g., during afirst stage of the training process) using medical images 204 of acertain type (e.g., a second type) of tubular structures. Medical images204 may include, for example, optical images of the retina and the typeof tubular structures included in the medical images may include retinablood vessels depicted in the optical images. Medical images 204 may beobtained from a public database and may include corresponding annotated(e.g., labelled) segmentations 206 that may be used as ground truth forthe training. When referred to herein, annotated medical images mayreferred to medical images 204 and the annotated segmentations oftubular structures (e.g., segmentations 206) associated with the medicalimages. In examples, ANN 202 may receive (e.g., be provided with)medical image 204 during an iteration of the training and predictsegmentation 208 based on the received image and current operatingparameters (e.g., weights) of the network. Once predicted, segmentation208 may be compared to segmentation 206 (e.g., ground truth) and a lossassociated with the prediction may be determined based on thecomparison. The loss may be calculated, for example, using a suitableloss function such as one based on a cross entropy loss, a mean squarederror, an L1 norm, an L2 norm, etc. The calculated loss may then be usedto adjust the parameters of ANN 202, for example, by backpropagating theloss through ANN 202 (e.g., based on a gradient descent of the loss).

The operations described above may be repeated for multiple iterationsuntil certain criteria for terminating the training are met. Forexample, the criteria for terminating the training may be satisfied ifthe loss described above falls below a predetermined thresholds, if achange in the loss value between two training iterations (e.g., betweenconsecutive training iterations) falls below a predetermined threshold,etc. Upon completing the training, the operating parameters of ANN 202(e.g., constituting a segmentation model) may be deemed suitable forsegmenting the type of tubular structures (e.g., retina blood vessels)included in medical images 204. Subsequently, the segmentationcapabilities of ANN 202 (e.g., the segmentation model learned during thefirst stage of training) may be further expanded to cover other types oftubular structures such as blood vessels from other areas of the humanbody, artificially placed catheters and/or guild wires, etc.

FIG. 2B illustrates an example of training ANN 202 to process (e.g.,segment) other types of tubular structures using unlabeled (e.g.,without annotation) training images of the other types of tubularstructures. During the training (e.g., a second stage of the trainingprocess that may follow the first stage illustrated in FIG. 2A), ANN 202may receive (e.g., be provided with) medical image 224 comprising a type(e.g., a first type) of tubular structures (e.g., coronary arteries) andmay predict segmentation 226 based on the received image and thesegmentation model (e.g., operating parameters or weights of thenetwork) learned from the first stage. Segmentation 226 may then becorrected by segmentation correction module 228 (e.g., a pseudo-labelgenerator) based on one or more characteristics (e.g., predeterminedconstraints) of the first type of tubular structures to derivesegmentation 230, which may serve as the ground truth for howsegmentation 226 should have been generated for the first type oftubular structures. For example, segmentation 230 may be compared tosegmentation 226 to determine a difference (e.g., a loss) between thetwo segmentations and that difference may be used to guide theadjustment of the ANN’s operating parameters (e.g., the segmentationmodel) such that those parameters may be further optimized (e.g., fromthose learned using medical images 202 of FIG. 2A) to fit thesegmentation generated by ANN 202 to corrected segmentation 230. Asdescribed herein, the difference or loss between segmentation 226 andsegmentation 230 may be determined using a loss function based on MSE,L1 norm, L2 norm, and/or the like. And once determined, the loss may bebackpropagated through ANN 202 (e.g., based on a gradient descentassociated with the loss) to adjust the parameters of the neuralnetwork.

Segmentation correction module 228 (e.g., a pseudo-label generator) maybe implemented using software and/or hardware components to realize thefunctionalities described above, and the correction of segmentation 226may be performed based on characteristics (e.g., constraints) of thefirst type of tubular structures that may be used to distinguish theareas that include the first type of tubular structures from the areasthat do not include the first type of tubular structures. FIG. 3A andFIG. 3B illustrate example techniques for correcting the segmentationpredicted by ANN 202 based on characteristics of the first type oftubular structures.

FIG. 3A illustrates an example of correcting a segmentation (e.g.,segmentation 226 shown in FIG. 2B) based on the respective sizes of oneor more connected regions that correspond to identified tubularstructures in the segmentation. The sizes may be used to distinguish thetubular structures because a true tubular structure (e.g., blood vessel,guide wire, etc.) may be occupy a larger area (e.g., a greater length)than a falsely identified tubular structure (e.g., an artifactresembling the target tubular structure). As shown in FIG. 3A, asegmentation correction module (e.g., segmentation correction module228) may be configured to detect multiple connected regions (e.g., 302a, 302 b, 302 c, etc.) in segmentation 302 that correspond to identifiedtubular structures by identifying a respective number of connectedpixels (e.g., connected pixels) in each of the regions. For example, thesegmentation correction module may identify connected region 302 a byidentifying a first pixel in the region that is indicated as belongingto the tubular structure and further identifying a chain of consecutivepixels connected to the first pixel that are also indicated as belongingto the tubular structure, until the chain breaks (e.g., by a pixelindicated as not belonging to the tubular structure). The segmentationcorrection module may then treat the area occupied by the chain ofpixels (including the first pixel) as connected region 302 a.

Using similar techniques, the segmentation correction module mayidentify other connected regions (e.g., 302 b and 302 c) and may furtherdetermine the size of each connected region (e.g., connected region 302a, 302 b, 302 c, etc.), for example, by counting the number of pixelsincluded in each region. The segmentation correction module may thendetermine whether each of the connected regions truly should be labeledas a tubular structure region (e.g., including the target tubularstructures) based on the size of the connected region. For example, thesegmentation correction module may determine that connected region 302 ais a tubular structure region if the size of connected region 302 a isabove a threshold (e.g., a preset threshold). The segmentationcorrection module may further determine that connected regions 302 b and302 c are falsely identified as tubular structure regions if therespective sizes of connected regions 302 b and 302 c are below thethreshold. Responsive to making such determinations, the segmentationcorrection module may, in corrected segmentation 304, maintain thelabeling of region 302 a as a tubular structure region and change thelabeling of regions 302 b and 302 c as non-tubular structure regions.

FIG. 3B illustrates an example of correcting a segmentation (e.g.,segmentation 226 shown in FIG. 2B) based on motion information (e.g.,inter-frame signal difference) associated with identified tubularstructures in the segmentation. The motion information may be used todistinguish the tubular structures because a tubular structure ofinterest (e.g., blood vessel, guide wire, etc.) may exhibit largermotions from one image frame to the next compared to other parts of theimage frames. As shown in FIG. 3B, the motion of one or more targettubular structures may be determined based on multiple medical images322 a and 322 b comprising the tubular structures. Medical images 322 aand 322 b may be adjacent image frames or may be separated by one ormore other image frames and as described herein, medical image 332 a maybe processed through ANN 324 (e.g., ANN 202 of FIG. 2B) to obtain asegmentation 325 (e.g., a binary map) of the tubular structures. Basedon the obtained segmentation 325 and/or the input medical image(s)(e.g., 322 a and 322 b), segmentation correction module 326 (e.g.,segmentation correction module 228 of FIG. 2B) may determine changes(e.g., disparity or displacement of features which may indicate a signalchange, a pixel-wise displacement, etc.) that may have occurred in oneor more areas (e.g., tubular and/or non-tubular areas) of segmentation325, and segmentation correction module 326 may further determinemotions associated with the one or more areas based on the changes.Segmentation correction module 326 may assess the changes, for example,by including a motion calculation module 326 a configured to calculatethe magnitude of a motion field or flow field 326 b based on one or morefeatures (e.g., image gradients and/or inter-frame pixel intensitydifferences) of medical images 322 a and 322 b. In examples, such amotion field or flow field may include a vector, a grid of vectors, avector-value function, and/or the like that may indicate the disparityor displacement of features from medical image 322 a to medical image322 b for the tubular and non-tubular objects depicted in the medicalimages.

Since the disparity or displacement of features (e.g., or lack thereof)may be indicative of the respective motions (or lack of motions) of theobjects depicted in medical images 322 a and 322 b and the tubularstructures of interest in the images may (e.g., inherently) have largermotions than the non-tubular structures in the images, segmentationcorrection module 328 may be able to distinguish the true tubularstructures in segmentation 325 from the falsely identified tubularstructures based on motion field or flow field 326 b. For example,segmentation correction module 326 may include a motion averaging module326 c configured to generate a motion map 326 d, in which each pixel maycontain a value that depicts the motion (e.g., an average motioncalculated from multiple images) of a connected region in segmentation325 to which the pixel may belong. Segmentation correction module 326may further include a motion thresholding module 326 e that may beconfigured to determine that the areas of segmentation 325 that havelarge motions (e.g., above a threshold) are correctly labeled as tubularstructures and therefore the labeling for these areas should bemaintained as such in corrected segmentation 327. Motion thresholdingmodule 326 e may further determine that the areas of segmentation 325that have smaller motions (e.g., below a threshold) are incorrectlylabeled as tubular structures and therefore those areas should berelabeled as non-tubular areas in corrected segmentation 327. Motionthresholding module 326 e may use the motion of a background area as thethreshold for correcting the labeling in segmentation 325. For example,motion thresholding module 326 e may determine that an area is correctlylabeled as including the tubular structures of interest if the areaexhibits larger motions than the background area and that an area isincorrectly labeled as including the tubular structures of interest ifthe area exhibits the same or smaller motions than the background area.Motion thresholding module 326 e may also use a preset (e.g.,pre-configured or pre-determined) motion threshold for correcting thelabeling in segmentation 325.

Various techniques may be applied to improve the accuracy of the motionestimation described above. For example, background pixels ofsegmentation 325 (e.g., pixels labeled as 0 in the binary map thatindicates the segmentation) may be registered using optical flow basedtechniques to stabilize the background from frame to frame (e.g., keepstationary objects in the background at the same image coordinates fromframe to frame) so that the motion of the tubular structures in theforeground may be readily determined. Furthermore, labeled medicalimages of the target tubular structures, if available, may be used alongwith the publicly available labeled training images described herein(e.g., the retina blood vessel images) to train the segmentation neuralnetworks described herein, which may accelerate the transfer learningprocess described herein and/or improve the segmentation capabilities ofthe neural networks.

FIG. 4 illustrates example operations that may be associated withtraining a neural network (e.g., ANN 102 of FIG. 1 and/or ANN 202 ofFIGS. 2A and 2B) to perform the segmentation tasks described herein. Asshown, the training operations may include initializing the parametersof the neural network (e.g., weights associated with the various filtersor kernels of the neural network) at 402. The parameters may beinitialized, for example, based on samples collected from one or moreprobability distributions or parameter values of another neural networkhaving a similar architecture. The training operations may furtherinclude providing training data (e.g., publicly available labeledmedical images comprising a type of tubular structures) to the neuralnetwork at 404, and causing the neural network to predict a segmentationat 406. At 408, the predicted segmentation may be compared with a groundtruth such as an annotated ground truth segmentation (e.g., segmentation206 shown in FIG. 2A) or a segmentation generated using the techniquesdescribed herein (e.g., corrected segmentation 230 of FIG. 2B) todetermine a loss associated with the prediction. The loss may bedetermined using a suitable loss function such as, e.g., a loss functionbased on mean squared errors (MSE), L1 norm, L2 norm, etc. Oncedetermined, the loss may be evaluated at 410 to determine whether one ormore training termination criteria have been satisfied. For example, atraining termination criterion may be deemed satisfied if the loss(es)described above is below a predetermined thresholds, if a change in theloss(es) between two training iterations (e.g., between consecutivetraining iterations) falls below a predetermined threshold, etc. If thedetermination at 410 is that the training termination criterion has beensatisfied, the training may end. Otherwise, the loss may bebackpropagated (e.g., based on a gradient descent associated with theloss) through the neural network at 412 before the training returns to406.

For simplicity of explanation, the training steps are depicted anddescribed herein with a specific order. It should be appreciated,however, that the training operations may occur in various orders,concurrently, and/or with other operations not presented or describedherein. Furthermore, it should be noted that not all operations that maybe included in the training process are depicted and described herein,and not all illustrated operations are required to be performed.

The systems, methods, and/or instrumentalities described herein may beimplemented using one or more processors, one or more storage devices,and/or other suitable accessory devices such as display devices,communication devices, input/output devices, etc. FIG. 5 is a blockdiagram illustrating an example apparatus 500 that may be configured toperform the tubular structure segmentation tasks described herein. Asshown, apparatus 500 may include a processor (e.g., one or moreprocessors) 502, which may be a central processing unit (CPU), agraphics processing unit (GPU), a microcontroller, a reduced instructionset computer (RISC) processor, application specific integrated circuits(ASICs), an application-specific instruction-set processor (ASIP), aphysics processing unit (PPU), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), or any other circuit or processorcapable of executing the functions described herein. Apparatus 500 mayfurther include a communication circuit 504, a memory 506, a massstorage device 508, an input device 510, and/or a communication link 512(e.g., a communication bus) over which the one or more components shownin the figure may exchange information.

Communication circuit 504 may be configured to transmit and receiveinformation utilizing one or more communication protocols (e.g., TCP/IP)and one or more communication networks including a local area network(LAN), a wide area network (WAN), the Internet, a wireless data network(e.g., a Wi-Fi, 3G, 4G/LTE, or 5G network). Memory 606 may include astorage medium (e.g., a non-transitory storage medium) configured tostore machine-readable instructions that, when executed, cause processor502 to perform one or more of the functions described herein. Examplesof the machine-readable medium may include volatile or non-volatilememory including but not limited to semiconductor memory (e.g.,electrically programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM)), flash memory, and/orthe like. Mass storage device 508 may include one or more magnetic diskssuch as one or more internal hard disks, one or more removable disks,one or more magneto-optical disks, one or more CD-ROM or DVD-ROM disks,etc., on which instructions and/or data may be stored to facilitate theoperation of processor 502. Input device 510 may include a keyboard, amouse, a voice-controlled input device, a touch sensitive input device(e.g., a touch screen), and/or the like for receiving user inputs toapparatus 500.

It should be noted that apparatus 500 may operate as a standalone deviceor may be connected (e.g., networked, or clustered) with othercomputation devices to perform the functions described herein. And eventhough only one instance of each component is shown in FIG. 5 , askilled person in the art will understand that apparatus 500 may includemultiple instances of one or more of the components shown in the figure.

While this disclosure has been described in terms of certain embodimentsand generally associated methods, alterations and permutations of theembodiments and methods will be apparent to those skilled in the art.Accordingly, the above description of example embodiments does notconstrain this disclosure. Other changes, substitutions, and alterationsare also possible without departing from the spirit and scope of thisdisclosure. In addition, unless specifically stated otherwise,discussions utilizing terms such as “analyzing,” “determining,”“enabling,” “identifying,” “modifying” or the like, refer to the actionsand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(e.g., electronic) quantities within the computer system’s registers andmemories into other data represented as physical quantities within thecomputer system memories or other such information storage, transmissionor display devices.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other implementations will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. An apparatus, comprising: one or more processorsconfigured to: receive a medical image, wherein the medical imagedepicts a first type of tubular structures; and segment the first typeof tubular structures from the medical image using an artificial neuralnetwork (ANN), wherein the ANN is trained to segment the first type oftubular structures through a training process that comprises: trainingthe ANN during a first stage of the training process to learn asegmentation model for segmenting a second type of tubular structuresbased on annotated medical images of the second type of tubularstructures; and further training the ANN during a second stage of thetraining process to segment the first type of tubular structures basedon the segmentation model learned from the first stage of the trainingprocess, wherein the second stage of the training process comprises:providing a first training image comprising the first type of tubularstructures to the ANN; causing the ANN to generate a first segmentationof the first type of tubular structures based on the first trainingimage and the segmentation model learned from the first stage of thetraining process; correcting the first segmentation generated by the ANNbased on one or more characteristics of the first type of tubularstructures to derive a corrected segmentation; and causing the ANN toadjust the segmentation model based on a difference between the firstsegmentation generated by the ANN and the corrected segmentation.
 2. Theapparatus of claim 1, wherein correcting the first segmentation based onthe one or more characteristics of the first type of tubular structurescomprises: identifying one or more connected regions that correspond tothe first type of tubular structures in the first segmentation;determining a respective size of each of the one or more connectedregions; and indicating, in the corrected segmentation, whether each ofthe one or more connected regions includes the first type of tubularstructures based on the size of the connected region, wherein theconnected region is indicated as including the first type of tubularstructures on a condition that the size of the connected region is abovea threshold and wherein the connected region is indicated as notincluding the first type of tubular structures on a condition that thesize of the connected region is below the threshold.
 3. The apparatus ofclaim 2, wherein the respective size of each of the one or moreconnected regions is determined based on a respective number of pixelscomprised in the each of the one or more connected regions.
 4. Theapparatus of claim 2, wherein identifying the one or more connectedregions that correspond to the first type of tubular structures in thefirst segmentation comprises identifying a consecutive number of pixelsin the first segmentation that are indicated as belonging to the firsttype of tubular structures and treating an area occupied by theconsecutive number of pixels as a connected region.
 5. The apparatus ofclaim 1, wherein correcting the first segmentation based on the one ormore characteristics of the first type of tubular structures comprises:determining a motion of an area of the first segmentation that isindicated as including the first type of tubular structures; andindicating, in the corrected segmentation, whether the area includes thefirst type of tubular structures based on the motion of the area.
 6. Theapparatus of claim 5, wherein determining the motion of the areacomprises calculating a change between the first training image and asecond training image that includes the first type of tubularstructures.
 7. The apparatus of claim 6, wherein determining the motionof the area further comprises registering the first training image withthe second training image.
 8. The apparatus of claim 5, whereincorrecting the first segmentation based on the one or morecharacteristics of the first type of tubular structures comprisesdetermining that the area of the first segmentation that is indicated asincluding the first type of tubular structures has a larger motion thana background area of the first segmentation and labeling the area asincluding the first type of tubular structures in the correctedsegmentation based on the determination.
 9. The apparatus of claim 5,wherein correcting the first segmentation based on the one or morecharacteristics of the first type of tubular structures comprisesdetermining that the area of the first segmentation that is indicated asincluding the first type of tubular structures has a substantiallysimilar motion as a background area of the first segmentation andlabeling the area as not including the first type of tubular structuresin the corrected segmentation based on the determination.
 10. Theapparatus of claim 1, wherein the first type of tubular structuresincludes coronary vessels, a catheter placed in a human body, or a guidewire placed in the human body and wherein the second type of tubularstructures includes blood vessels located in a different area of thehuman body than the coronary vessels.
 11. The apparatus of claim 1,wherein the medical image includes an X-ray fluoroscopic image.
 12. Amethod for segmenting tubular structures, the method comprising:receiving a medical image, wherein the medical image depicts a firsttype of tubular structures; and segmenting the first type of tubularstructures from the medical image using an artificial neural network(ANN), wherein the ANN is trained to segment the first type of tubularstructures through a training process that comprises: training the ANNduring a first stage of the training process to learn a segmentationmodel for segmenting a second type of tubular structures based onannotated medical images of the second type of tubular structures; andfurther training the ANN during a second stage of the training processto segment the first type of tubular structures based on thesegmentation model learned from the first stage of the training process,wherein the second stage of the training process comprises: providing afirst training image comprising the first type of tubular structures tothe ANN; causing the ANN to generate a first segmentation of the firsttype of tubular structures based on the first training image and thesegmentation model learned from the first stage of the training process;correcting the first segmentation generated by the ANN based on one ormore characteristics of the first type of tubular structures to derive acorrected segmentation; and causing the ANN to adjust the segmentationmodel based on a difference between the first segmentation generated bythe ANN and the corrected segmentation.
 13. The method of claim 12,wherein correcting the first segmentation based on the one or morecharacteristics of the first type of tubular structures comprises:identifying one or more connected regions that correspond to the firsttype of tubular structures in the first segmentation; determining arespective size of each of the one or more connected regions; andindicating, in the corrected segmentation, whether each of the one ormore connected regions includes the first type of tubular structuresbased on the size of the connected region, wherein the connected regionis indicated as including the first type of tubular structures on acondition that the size of the connected region is above a threshold andwherein the connected region is indicated as not including the firsttype of tubular structures on a condition that the size of the connectedregion is below the threshold.
 14. The method of claim 13, wherein therespective size of each of the one or more connected regions isdetermined based on a respective number of pixels comprised in the eachof the one or more connected regions.
 15. The method of claim 13,wherein identifying the one or more connected regions that correspond tothe first type of tubular structures in the first segmentation comprisesidentifying a consecutive number of pixels in the first segmentationthat are indicated as belonging to the first type of tubular structuresand treating an area occupied by the consecutive number of pixels as aconnected region.
 16. The method of claim 12, wherein correcting thefirst segmentation based on the one or more characteristics of the firsttype of tubular structures comprises: determining a motion of an area ofthe first segmentation that is indicated as including the first type oftubular structures; and indicating, in the corrected segmentation,whether the area includes the first type of tubular structures based onthe motion of the area.
 17. The method of claim 16, wherein determiningthe motion of the area comprises calculating a change between the firsttraining image and a second training image that includes the first typeof tubular structures.
 18. The method of claim 16, wherein correctingthe first segmentation based on the one or more characteristics of thefirst type of tubular structures comprises determining that the area ofthe first segmentation that is indicated as including the first type oftubular structures has a larger motion than a background area of thefirst segmentation and labeling the area as including the first type oftubular structures in the corrected segmentation based on thedetermination.
 19. The method of claim 16, wherein correcting the firstsegmentation based on the one or more characteristics of the first typeof tubular structures comprises determining that the area of the firstsegmentation that is indicated as including the first type of tubularstructures has a substantially similar motion as a background area ofthe first segmentation and labeling the area as not including the firsttype of tubular structures in the corrected segmentation based on thedetermination.
 20. A method for training an artificial neural network(ANN) to segment a first type of tubular structures, the methodcomprising: training the ANN during a first stage of a training processto learn a segmentation model for segmenting a second type of tubularstructures based on annotated medical images of the second type oftubular structures; and further training the ANN during a second stageof the training process to segment the first type of tubular structuresbased on the segmentation model learned from the first stage of thetraining process, wherein the second stage of the training processcomprises: providing a first training image comprising the first type oftubular structures to the ANN; causing the ANN to generate a firstsegmentation of the first type of tubular structures based on the firsttraining image and the segmentation model learned from the first stageof the training process; correcting the first segmentation generated bythe ANN based on one or more characteristics of the first type oftubular structures to derive a corrected segmentation; and causing theANN to adjust the segmentation model based on a difference between thefirst segmentation generated by the ANN and the corrected segmentation.